First-Order Logic with Isomorphism

Total Page:16

File Type:pdf, Size:1020Kb

First-Order Logic with Isomorphism FIRST-ORDER LOGIC WITH ISOMORPHISM DIMITRIS TSEMENTZIS Abstract. The Univalent Foundations requires a logic that allows us to define structures on homotopy types, similar to how first-order logic with equality (FOL=) allows us to define structures on sets. We develop the syntax, semantics and de- ductive system for such a logic, which we call first-order logic with isomorphism (FOL=∼). The syntax of FOL=∼ extends FOL= in two ways. First, by incorporating into its signatures a notion of dependent sorts along the lines of Makkai’s FOLDS as well as a notion of an h-level of each sort. Second, by specifying three new logical sorts within these signatures: isomorphism sorts, reflexivity predicates and trans- port structure. The semantics for FOL=∼ is then defined in homotopy type theory with the isomorphism sorts interpreted as identity types, reflexivity predicates as relations picking out the trivial path, and transport structure as transport along a path. We then define a deductive system D=∼ for FOL∼= that encodes the sense in which the inhabitants of isomorphism sorts really do behave like isomorphisms and prove soundness of the rules of D=∼ with respect to its homotopy semantics. Fi- nally, as an application, we prove that precategories, strict categories and univalent categories are axiomatizable in FOL∼=. Contents Introduction 1 1. Preliminaries on FOLDS 3 2. Semantics of FOLDS in MLTT 14 3. Syntax of FOL=∼ 25 4. Homotopy Semantics of FOL=∼ 35 5. Deductive System for FOL=∼ 47 6. Examples and Applications 55 References 61 arXiv:1603.03092v2 [math.LO] 25 Sep 2017 Introduction The Univalent Foundations of Mathematics (UF) [Uni13] take their basic objects to be homotopy types. In UF mathematical structures are therefore encoded as struc- tured homotopy types, similar to how in set-theoretic foundations they are encoded as structured sets. This basic picture allows us to envision a model theory in which Date: September 27, 2017. 2010 Mathematics Subject Classification. 03G99, 03B15, 03B22, 03C99. Key words and phrases. Univalent Foundations, Categorical Logic, Homotopy Type Theory. 1 2 DIMITRIS TSEMENTZIS mathematical objects are no longer described as structured sets, but rather as struc- tured homotopy types. In this paper we develop a logic through which this process can be carried out. We will call it first-order logic with isomorphism (FOL=∼) in order to reflect the following fundamental idea of UF: the primitive notion of equality in UF is itself a structure that behaves like the structure of isomorphisms between structures, rather than the fact of identity between sets. The syntax of FOL=∼ will be based on an extension of the syntax of Makkai’s FOLDS (First-Order Logic with Dependent Sorts) [Mak95]. The signatures of FOLDS can be understood as inverse categories where the arrows encode variable dependencies between the objects (understood as “sorts”). The key addition of FOL=∼ to FOLDS is to add a notion of an “h-level” to each sort as well as notions of “isomorphism sorts”, “reflexivity predicates” and “transport structure” understood as logical sorts (i.e. with a fixed denotation) defined on top of the non-logical sorts in accordance with the latter’s h-level. It is helpful to think of the syntax of FOL=∼ as standing to FOLDS in the same relation that the syntax of first-order logic with equality (FOL=) stands to plain first-order logic. To add equality to a first-order signature Σ, one simply adds a binary relation with a certain fixed denotation. The analogous process for a FOLDS signature L is carried out in terms of an “isomorphism completion” operation on inverse categories which attaches logical sorts to pre-existing sorts in L in a manner compatible with their h-level. The intended semantics for FOL=∼ is in UF, where in particular “isomorphism sorts” are interpreted as path spaces (if one thinks in terms of abstract homotopy theory) or identity types (if one thinks in terms of homotopy type theory). The basic idea of the “homotopy semantics” of FOL=∼ is the following: non-logical sorts of h-level m are interpreted as (dependent functions landing in) types of h-level m and we make this precise by interpreting FOL=∼ into a version of “book HoTT”, i.e. the formal system outlined in [Uni13]. The isomorphism sorts that have been added to the syntax of FOL=∼ are then interpreted as identity types, the reflexivity predicates are interpreted as predicates picking out refl and transport structure is interpreted as (a relational version of) the transport function trans induced by a given path. Finally, we present a deductive system D=∼ on the syntax of FOL=∼ that can justify its homotopy semantics. It is based on a standard sequent calculus for first-order logic to which we add three rules: a rule asserting the existence of the “reflexivity” isomorphisms, a rule specifying that “transport along reflexivity” does nothing and, crucially, a propositional version of the J-rule of MLTT. We can then prove that the deductive system is sound with respect to the above-sketched semantics, which is the main result of this paper. Related Work. Our work connects to several strands of ongoing work in UF. The connection between inverse categories and homotopy theory is well-known and has been developed extensively by Shulman in [Shu15a,Shu15b] and our work builds on this connection, albeit from a more syntactic viewpoint, rather than within the categorical semantics of HoTT. Our framework also provides a general definition of a FIRST-ORDERLOGICWITHISOMORPHISM 3 (logical) signature for UF and could thus be used to generalize the Structure Identity Principle of [Uni13] as indeed has been sketched in [ANS14, Tse17]. Formalizations of category theory in the style of FOLDS has also been carried out by Ahrens in UniMath [VAG+] and his formalization overlaps with some of the material of Sec- tion 6. Furthermore, our system FOL=∼ (as well as FOLDS) can be thought of as a dependently-typed first-order logic with specific features that make it suitable for UF, and in that regard it connects to the work of Palmgren [Pal16]. Finally, although we do not (yet) adopt this point of view, FOL=∼ can be thought of as a proposal to add a “logic layer” to HoTT along the lines of so-called logic-enriched type theory [GA06] and therefore is connected to the work of [PL15]. Outline of the Paper. In Section 1 we introduce the syntax of FOLDS as well as a proof system DFOLDS for this syntax and state some preliminary results. In Section 2 we define an interpretation of the syntax of FOLDS into MLTT via a direct interpretation of the raw syntax of the former system into the raw syntax of the latter. In Section 3 we build on the syntax of FOLDS to define the syntax of FOL=∼. In Section 4 we define the homotopy semantics of FOL=∼ by defining an interpretation of the syntax of FOL=∼ into HoTT, building on the interpretation of FOLDS into MLTT. In Section 5 we define a proof system D=∼ for FOL=∼ and prove soundness for D=∼ with respect to our homotopy semantics. Finally, in Section 6 we show how precategories, strict categories and univalent categories can be axiomatized in terms of FOL=∼. 1. Preliminaries on FOLDS We will assume familiarity with the basics of categorical logic as well as of in- tensional Martin-Löf Type Theory and its homotopy interpretation. For category- theoretic background [ML98] remains the standard reference; for the basics of de- pendent type theory we recommend [ML84, Hof97]; for the homotopy interpretation and an introduction to the Univalent Foundations see [Uni13,KLV14] and references therein. We will now present in more detail the basic syntax of FOLDS inspired by Makkai’s original presentation in [Mak95]. Definition 1.1 (Inverse Category). An inverse category is a category L such that (1) L has no non-identity endomorphisms (2) L is skeletal (3) For any object K there is a finite number of arrows with domain K Remark 1.2. An inverse category is so-named because it can be thought of as a category in which all arrows “go in one direction” and in particular a category in which all arrows “point downwards”. Condition (3) in Definition 1.1 (which Makkai called “finite fan-out”) ensures that every object in an inverse category is only a “finite path” away from an object at the “bottom”. This allows us to carry out inductive definitions on inverse categories as indeed we will be doing throughout this paper. Notation. We write N∞ for the set N ∪ {∞}. We will sometimes write K ∈ L as an abbreviation for K ∈ ObL and we write L(K,K′) for the morphisms from K to 4 DIMITRIS TSEMENTZIS ′ ′ K in L. For any arrow f : K → K in an inverse category L we will write Kf for its codomain K′. For any object K ∈ ObL we write K//L for the set (indeed, cosieve) of non-identity arrows with domain K. The objects of any inverse category L can be stratified into levels, defined (inductively) as follows: 0 if K is the domain only of 1 K l(K)= sup l(Kf ) + 1 otherwise f∈K//L Terminology. We will call l(K) the level of K. We will generally refer to the objects of L as sorts. We call a non-identity arrow f : K → A top-level (for K) if it does not factor through another arrow, i.e. if there is no non-identity arrows h, k ∈ MorL such that f = hk and we write top(K) for the set of top-level arrows of a sort K.
Recommended publications
  • Reasoning About Equations Using Equality
    Mathematics Instructional Plan – Grade 4 Reasoning About Equations Using Equality Strand: Patterns, Functions, Algebra Topic: Using reasoning to assess the equality in closed and open arithmetic equations Primary SOL: 4.16 The student will recognize and demonstrate the meaning of equality in an equation. Related SOL: 4.4a Materials: Partners Explore Equality and Equations activity sheet (attached) Vocabulary equal symbol (=), equality, equation, expression, not equal symbol (≠), number sentence, open number sentence, reasoning Student/Teacher Actions: What should students be doing? What should teachers be doing? 1. Activate students’ prior knowledge about the equal sign. Formally assess what students already know and what misconceptions they have. Ask students to open their notebooks to a clean sheet of paper and draw lines to divide the sheet into thirds as shown below. Create a poster of the same graphic organizer for the room. This activity should take no more than 10 minutes; it is not a teaching opportunity but simply a data- collection activity. Equality and the Equal Sign (=) I know that- I wonder if- I learned that- 1 1 1 a. Let students know they will be creating a graphic organizer about equality and the equal sign; that is, what it means and how it is used. The organizer is to help them keep track of what they know, what they wonder, and what they learn. The “I know that” column is where they can write things they are pretty sure are correct. The “I wonder if” column is where they can write things they have questions about and also where they can put things they think may be true but they are not sure.
    [Show full text]
  • Indexed Induction-Recursion
    Indexed Induction-Recursion Peter Dybjer a;? aDepartment of Computer Science and Engineering, Chalmers University of Technology, 412 96 G¨oteborg, Sweden Email: [email protected], http://www.cs.chalmers.se/∼peterd/, Tel: +46 31 772 1035, Fax: +46 31 772 3663. Anton Setzer b;1 bDepartment of Computer Science, University of Wales Swansea, Singleton Park, Swansea SA2 8PP, UK, Email: [email protected], http://www.cs.swan.ac.uk/∼csetzer/, Tel: +44 1792 513368, Fax: +44 1792 295651. Abstract An indexed inductive definition (IID) is a simultaneous inductive definition of an indexed family of sets. An inductive-recursive definition (IRD) is a simultaneous inductive definition of a set and a recursive definition of a function from that set into another type. An indexed inductive-recursive definition (IIRD) is a combination of both. We present a closed theory which allows us to introduce all IIRDs in a natural way without much encoding. By specialising it we also get a closed theory of IID. Our theory of IIRDs includes essentially all definitions of sets which occur in Martin-L¨of type theory. We show in particular that Martin-L¨of's computability predicates for dependent types and Palmgren's higher order universes are special kinds of IIRD and thereby clarify why they are constructively acceptable notions. We give two axiomatisations. The first and more restricted one formalises a prin- ciple for introducing meaningful IIRD by using the data-construct in the original version of the proof assistant Agda for Martin-L¨of type theory. The second one admits a more general form of introduction rule, including the introduction rule for the intensional identity relation, which is not covered by the restricted one.
    [Show full text]
  • Formal Systems: Combinatory Logic and -Calculus
    INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS:COMBINATORY LOGIC AND λ-CALCULUS Andrew R. Plummer Department of Linguistics The Ohio State University 30 Sept., 2009 INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION OUTLINE 1 INTRODUCTION 2 APPLICATIVE SYSTEMS 3 USEFUL INFORMATION INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION COMBINATORY LOGIC We present the foundations of Combinatory Logic and the λ-calculus. We mean to precisely demonstrate their similarities and differences. CURRY AND FEYS (KOREAN FACE) The material discussed is drawn from: Combinatory Logic Vol. 1, (1958) Curry and Feys. Lambda-Calculus and Combinators, (2008) Hindley and Seldin. INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS We begin with some definitions. FORMAL SYSTEMS A formal system is composed of: A set of terms; A set of statements about terms; A set of statements, which are true, called theorems. INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS TERMS We are given a set of atomic terms, which are unanalyzed primitives. We are also given a set of operations, each of which is a mode for combining a finite sequence of terms to form a new term. Finally, we are given a set of term formation rules detailing how to use the operations to form terms. INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS STATEMENTS We are given a set of predicates, each of which is a mode for forming a statement from a finite sequence of terms. We are given a set of statement formation rules detailing how to use the predicates to form statements. INTRODUCTION APPLICATIVE SYSTEMS USEFUL INFORMATION FORMAL SYSTEMS THEOREMS We are given a set of axioms, each of which is a statement that is unconditionally true (and thus a theorem).
    [Show full text]
  • Wittgenstein, Turing and Gödel
    Wittgenstein, Turing and Gödel Juliet Floyd Boston University Lichtenberg-Kolleg, Georg August Universität Göttingen Japan Philosophy of Science Association Meeting, Tokyo, Japan 12 June 2010 Wittgenstein on Turing (1946) RPP I 1096. Turing's 'Machines'. These machines are humans who calculate. And one might express what he says also in the form of games. And the interesting games would be such as brought one via certain rules to nonsensical instructions. I am thinking of games like the “racing game”. One has received the order "Go on in the same way" when this makes no sense, say because one has got into a circle. For that order makes sense only in certain positions. (Watson.) Talk Outline: Wittgenstein’s remarks on mathematics and logic Turing and Wittgenstein Gödel on Turing compared Wittgenstein on Mathematics and Logic . The most dismissed part of his writings {although not by Felix Mülhölzer – BGM III} . Accounting for Wittgenstein’s obsession with the intuitive (e.g. pictures, models, aspect perception) . No principled finitism in Wittgenstein . Detail the development of Wittgenstein’s remarks against background of the mathematics of his day Machine metaphors in Wittgenstein Proof in logic is a “mechanical” expedient Logical symbolisms/mathematical theories are “calculi” with “proof machinery” Proofs in mathematics (e.g. by induction) exhibit or show algorithms PR, PG, BB: “Can a machine think?” Language (thought) as a mechanism Pianola Reading Machines, the Machine as Symbolizing its own actions, “Is the human body a thinking machine?” is not an empirical question Turing Machines Turing resolved Hilbert’s Entscheidungsproblem (posed in 1928): Find a definite method by which every statement of mathematics expressed formally in an axiomatic system can be determined to be true or false based on the axioms.
    [Show full text]
  • First-Order Logic
    Chapter 5 First-Order Logic 5.1 INTRODUCTION In propositional logic, it is not possible to express assertions about elements of a structure. The weak expressive power of propositional logic accounts for its relative mathematical simplicity, but it is a very severe limitation, and it is desirable to have more expressive logics. First-order logic is a considerably richer logic than propositional logic, but yet enjoys many nice mathemati- cal properties. In particular, there are finitary proof systems complete with respect to the semantics. In first-order logic, assertions about elements of structures can be ex- pressed. Technically, this is achieved by allowing the propositional symbols to have arguments ranging over elements of structures. For convenience, we also allow symbols denoting functions and constants. Our study of first-order logic will parallel the study of propositional logic conducted in Chapter 3. First, the syntax of first-order logic will be defined. The syntax is given by an inductive definition. Next, the semantics of first- order logic will be given. For this, it will be necessary to define the notion of a structure, which is essentially the concept of an algebra defined in Section 2.4, and the notion of satisfaction. Given a structure M and a formula A, for any assignment s of values in M to the variables (in A), we shall define the satisfaction relation |=, so that M |= A[s] 146 5.2 FIRST-ORDER LANGUAGES 147 expresses the fact that the assignment s satisfies the formula A in M. The satisfaction relation |= is defined recursively on the set of formulae.
    [Show full text]
  • Part 3: First-Order Logic with Equality
    Part 3: First-Order Logic with Equality Equality is the most important relation in mathematics and functional programming. In principle, problems in first-order logic with equality can be handled by, e.g., resolution theorem provers. Equality is theoretically difficult: First-order functional programming is Turing-complete. But: resolution theorem provers cannot even solve problems that are intuitively easy. Consequence: to handle equality efficiently, knowledge must be integrated into the theorem prover. 1 3.1 Handling Equality Naively Proposition 3.1: Let F be a closed first-order formula with equality. Let ∼ 2/ Π be a new predicate symbol. The set Eq(Σ) contains the formulas 8x (x ∼ x) 8x, y (x ∼ y ! y ∼ x) 8x, y, z (x ∼ y ^ y ∼ z ! x ∼ z) 8~x,~y (x1 ∼ y1 ^ · · · ^ xn ∼ yn ! f (x1, : : : , xn) ∼ f (y1, : : : , yn)) 8~x,~y (x1 ∼ y1 ^ · · · ^ xn ∼ yn ^ p(x1, : : : , xn) ! p(y1, : : : , yn)) for every f /n 2 Ω and p/n 2 Π. Let F~ be the formula that one obtains from F if every occurrence of ≈ is replaced by ∼. Then F is satisfiable if and only if Eq(Σ) [ fF~g is satisfiable. 2 Handling Equality Naively By giving the equality axioms explicitly, first-order problems with equality can in principle be solved by a standard resolution or tableaux prover. But this is unfortunately not efficient (mainly due to the transitivity and congruence axioms). 3 Roadmap How to proceed: • Arbitrary binary relations. • Equations (unit clauses with equality): Term rewrite systems. Expressing semantic consequence syntactically. Entailment for equations. • Equational clauses: Entailment for clauses with equality.
    [Show full text]
  • Logic, Sets, and Proofs David A
    Logic, Sets, and Proofs David A. Cox and Catherine C. McGeoch Amherst College 1 Logic Logical Statements. A logical statement is a mathematical statement that is either true or false. Here we denote logical statements with capital letters A; B. Logical statements be combined to form new logical statements as follows: Name Notation Conjunction A and B Disjunction A or B Negation not A :A Implication A implies B if A, then B A ) B Equivalence A if and only if B A , B Here are some examples of conjunction, disjunction and negation: x > 1 and x < 3: This is true when x is in the open interval (1; 3). x > 1 or x < 3: This is true for all real numbers x. :(x > 1): This is the same as x ≤ 1. Here are two logical statements that are true: x > 4 ) x > 2. x2 = 1 , (x = 1 or x = −1). Note that \x = 1 or x = −1" is usually written x = ±1. Converses, Contrapositives, and Tautologies. We begin with converses and contrapositives: • The converse of \A implies B" is \B implies A". • The contrapositive of \A implies B" is \:B implies :A" Thus the statement \x > 4 ) x > 2" has: • Converse: x > 2 ) x > 4. • Contrapositive: x ≤ 2 ) x ≤ 4. 1 Some logical statements are guaranteed to always be true. These are tautologies. Here are two tautologies that involve converses and contrapositives: • (A if and only if B) , ((A implies B) and (B implies A)). In other words, A and B are equivalent exactly when both A ) B and its converse are true.
    [Show full text]
  • Tool 1: Gender Terminology, Concepts and Definitions
    TOOL 1 Gender terminology, concepts and def initions UNESCO Bangkok Office Asia and Pacific Regional Bureau for Education Mom Luang Pin Malakul Centenary Building 920 Sukhumvit Road, Prakanong, Klongtoei © Hadynyah/Getty Images Bangkok 10110, Thailand Email: [email protected] Website: https://bangkok.unesco.org Tel: +66-2-3910577 Fax: +66-2-3910866 Table of Contents Objectives .................................................................................................. 1 Key information: Setting the scene ............................................................. 1 Box 1: .......................................................................................................................2 Fundamental concepts: gender and sex ...................................................... 2 Box 2: .......................................................................................................................2 Self-study and/or group activity: Reflect on gender norms in your context........3 Self-study and/or group activity: Check understanding of definitions ................3 Gender and education ................................................................................. 4 Self-study and/or group activity: Gender and education definitions ...................4 Box 3: Gender parity index .....................................................................................7 Self-study and/or group activity: Reflect on girls’ and boys’ lives in your context .....................................................................................................................8
    [Show full text]
  • Comparison Identity Vs. Equality Identity Vs. Equality for Strings Notions of Equality Order
    Comparison Comparisons and the Comparable • Something that we do a lot Interface • Can compare all kinds of data with respect to all kinds of comparison relations . Identity . Equality Lecture 14 . Order CS211 – Spring 2006 . Lots of others Identity vs. Equality Identity vs. Equality for Strings • For primitive types (e.g., int, long, float, double, boolean) . == and != are equality tests • Quiz: What are the results of the following tests? • For reference types (i.e., objects) . "hello".equals("hello") true . == and != are identity tests . In other words, they test if the references indicate the same address . "hello" == "hello" true in the Heap . "hello" == new String("hello") false • For equality of objects: use the equals( ) method . equals( ) is defined in class Object . Any class you create inherits equals from its parent class, but you can override it (and probably want to) Notions of equality Order • A is equal to B if A can be substituted for B anywhere • For numeric primitives • For all other reference . Identical things must be equal: == implies equals (e.g., int, float, long, types double) • Immutable values are equal if they represent same value! . <, >, <=, >= do not work . Use <, >, <=, >= • Not clear you want them . (new Integer(2)).equals(new Integer(2)) to work: suppose we . == is not an abstract operation compare People • For reference types that Compare by name? • Mutable values can be distinguished by assignment. correspond to primitive Compare by height? class Foo { int f; Foo(int g) { f = g; } } weight? types Compare by SSN? Foo x = new Foo(2); . As of Java 5.0, Java does CUID? Foo y = new Foo(2); Autoboxing and Auto- .
    [Show full text]
  • Dismantling Racism in Mathematics Instruction Exercises for Educators to Reflect on Their Own Biases to Transform Their Instructional Practice
    A Pathway to MAY 2021 Equitable Math Instruction Dismantling Racism in Mathematics Instruction Exercises for educators to reflect on their own biases to transform their instructional practice STRIDE 1 Dismantling Racism STRIDE 1 in Mathematics Instruction This tool provides teachers an opportunity to examine THEMES their actions, beliefs, and values around teaching math- Teacher Beliefs ematics. The framework for deconstructing racism in GUIDING PRINCIPLES mathematics offers essential characteristics of antiracist Culturally relevant curricula and math educators and critical approaches to dismantling practices designed to increase access for students of color. white supremacy in math classrooms by making visi- Promoting antiracist ble the toxic characteristics of white supremacy culture mathematics instruction. (Jones and Okun 2001; dismantling Racism 2016) with respect to math. Building on the framework, teachers engage with critical praxis in order to shift their instruc- CONTENT DEVELOPERS tional beliefs and practices towards antiracist math ed- Sonia Michelle Cintron ucation. By centering antiracism, we model how to be Math Content Specialist UnboundEd antiracist math educators with accountability. Dani Wadlington Director of Mathematics Education Quetzal Education Consulting Andre ChenFeng HOW TO USE THIS TOOL Ph.D. Student Education at Claremont Graduate While primarily for math educa- • Teachers should use this work- University tors, this text advocates for a book to self-reflect on individual collective approach to dismantling
    [Show full text]
  • Quantum Set Theory Extending the Standard Probabilistic Interpretation of Quantum Theory (Extended Abstract)
    Quantum Set Theory Extending the Standard Probabilistic Interpretation of Quantum Theory (Extended Abstract) Masanao Ozawa∗ Graduate School of Information Science, Nagoya University Nagoya, Japan [email protected] The notion of equality between two observables will play many important roles in foundations of quantum theory. However, the standard probabilistic interpretation based on the conventional Born formula does not give the probability of equality relation for a pair of arbitrary observables, since the Born formula gives the probability distribution only for a commuting family of observables. In this paper, quantum set theory developed by Takeuti and the present author is used to systematically extend the probabilistic interpretation of quantum theory to define the probability of equality relation for a pair of arbitrary observables. Applications of this new interpretation to measurement theory are discussed briefly. 1 Introduction Set theory provides foundations of mathematics. All the mathematical notions like numbers, functions, relations, and structures are defined in the axiomatic set theory, ZFC (Zermelo-Fraenkel set theory with the axiom of choice), and all the mathematical theorems are required to be provable in ZFC [15]. Quan- tum set theory instituted by Takeuti [14] and developed by the present author [11] naturally extends the logical basis of set theory from classical logic to quantum logic [1]. Accordingly, quantum set theory extends quantum logical approach to quantum foundations from propositional logic to predicate logic and set theory. Hence, we can expect that quantum set theory will provide much more systematic inter- pretation of quantum theory than the conventional quantum logic approach [3]. The notion of equality between quantum observables will play many important roles in foundations of quantum theory, in particular, in the theory of measurement and disturbance [9, 10].
    [Show full text]
  • Warren Goldfarb, Notes on Metamathematics
    Notes on Metamathematics Warren Goldfarb W.B. Pearson Professor of Modern Mathematics and Mathematical Logic Department of Philosophy Harvard University DRAFT: January 1, 2018 In Memory of Burton Dreben (1927{1999), whose spirited teaching on G¨odeliantopics provided the original inspiration for these Notes. Contents 1 Axiomatics 1 1.1 Formal languages . 1 1.2 Axioms and rules of inference . 5 1.3 Natural numbers: the successor function . 9 1.4 General notions . 13 1.5 Peano Arithmetic. 15 1.6 Basic laws of arithmetic . 18 2 G¨odel'sProof 23 2.1 G¨odelnumbering . 23 2.2 Primitive recursive functions and relations . 25 2.3 Arithmetization of syntax . 30 2.4 Numeralwise representability . 35 2.5 Proof of incompleteness . 37 2.6 `I am not derivable' . 40 3 Formalized Metamathematics 43 3.1 The Fixed Point Lemma . 43 3.2 G¨odel'sSecond Incompleteness Theorem . 47 3.3 The First Incompleteness Theorem Sharpened . 52 3.4 L¨ob'sTheorem . 55 4 Formalizing Primitive Recursion 59 4.1 ∆0,Σ1, and Π1 formulas . 59 4.2 Σ1-completeness and Σ1-soundness . 61 4.3 Proof of Representability . 63 3 5 Formalized Semantics 69 5.1 Tarski's Theorem . 69 5.2 Defining truth for LPA .......................... 72 5.3 Uses of the truth-definition . 74 5.4 Second-order Arithmetic . 76 5.5 Partial truth predicates . 79 5.6 Truth for other languages . 81 6 Computability 85 6.1 Computability . 85 6.2 Recursive and partial recursive functions . 87 6.3 The Normal Form Theorem and the Halting Problem . 91 6.4 Turing Machines .
    [Show full text]